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  <h1>R语言完整的Lefse分析</h1>
  <h2>写在前面</h2>
  <p>怎么说呢&#xff0c;我在之前的推送中&#xff0c;在R语言中实现了LEFse分析过程。但是我没有将数据前处理和结果的物种分类图形做出来。这是一定要在R语言中操作的&#xff0c;要不然&#xff0c;仅仅是实现了lefse算法&#xff0c;也是没有什么作用。</p>
  <p>所以这篇贴子主要的工作在于&#xff1a;</p>
  <ul><li><p>使用扩增子注物种注释文件构造Lefse分析输入文件</p></li><li><p>使用R语言实现Lefse核型计算过程</p></li><li><p>使用R语言实现物种分类树额绘制&#xff0c;比graphlan更加强大。</p></li><li><p>全部分析提供原始数据&#xff0c;可重现</p></li></ul>
  <p>注意&#xff1a;</p>
  <p>物种注释不同数据库格式不同&#xff0c;如果你的注释文件同参考不同&#xff0c;转化为和示例文件相同的格式&#xff0c;方可进行后续操作。</p>
  <h3>所需R包</h3>
  <pre class="has"><code>library(&#34;tidyverse&#34;)<br />library(microbiomeViz)<br />library(ggtree)<br />library(tidyverse)<br />library(phyloseq)<br /><br />##--功能函数#---------<br /><br />  # 功能函数<br />  ### 提取OTU表格<br />  vegan_otu     OTU     if(taxa_are_rows(OTU)){<!-- --><br />      OTU     }<br />    return(as(OTU,&#34;matrix&#34;))<br />  }<br />  ### 添加OTU注释信息<br />  vegan_tax     tax <br />    return(as(tax,&#34;matrix&#34;))<br />  }</code></pre>
  <h3>整理LEFSE分析的数据</h3>
  <p>这里如果大家熟悉phyloseq对象&#xff0c;直接用这个phyloseq对象就是了</p>
  <pre class="has"><code>#导入数据<br />ps &#61; readRDS(&#34;../../data/ps_liu.rds&#34;)</code></pre>
  <h3>如果不熟悉phyloseq可以使用普通数据导入</h3>
  <pre class="has"><code># 导入otu表格<br /><br />otu &#61; read.delim(&#34;../../data/otutab.txt&#34;,row.names &#61; 1)<br />head(otu)<br />#导入注释文件<br />tax &#61; read.delim(&#34;../..//data/taxonomy.txt&#34;,row.names &#61; 1)<br />head(tax)<br />#导入分组文件<br />map &#61; read.delim(&#34;../../data/metadata.tsv&#34;,row.names &#61; 1)<br />head(map)<br /><br /># head(otu)<br />otu &#61; as.matrix(otu)<br />str(otu)<br /><br />tax &#61; as.matrix(tax)<br /># taxa_names(tax)<br /><br />ps                sample_data(map) ,<br />               tax_table(tax)<br />               # phy_tree(tree)<br />)</code></pre>
  <h3>没有不要用全部OTU&#xff0c;我按照序列数过滤&#xff0c;当然可以不过滤</h3>
  <pre class="has"><code># otu数量很多&#xff0c;所以选择一部分展示&#xff0c;一般树展示200个作用最为合适<br />ps &#61; filter_taxa(ps, function(x) sum(x ) &gt; 400 , TRUE)<br />ps</code></pre>
  <h3>提取注释文件</h3>
  <p>这一步是我我们准备Lefse物种注释表格整理的开始。</p>
  <pre class="has"><code>#-提取otu表和tax表格#--------<br />otu_table &#61; as.data.frame(t(vegan_otu(ps )))<br />tax_table &#61; (vegan_tax(ps ))</code></pre>
  <h3>整理物种注释文件</h3>
  <p>首先去除未注释出来的物种&#xff0c;这里为&#xff1a;Unassigned。</p>
  <p>其次将物种不同级别之间的分隔符号修改为|。</p>
  <p>再有将每一级的名称都加上前面级别的全部注释名称&#xff0c;形成结构。</p>
  <p>最后按照不同级别数据合并otu表格&#xff0c;然后将注释文件和otu文件合并。</p>
  <h4>初步整理&#xff0c;去除Unassigned</h4>
  <pre class="has"><code>tax_table[tax_table &#61;&#61; &#34;Unassigned&#34;] &#61; NA<br /><br />tax_table &#61; as.data.frame(<br />  tax_table<br />)</code></pre>
  <h4>物种数据再整理</h4>
  <p>其次将物种不同级别之间的分隔符号修改为|。</p>
  <pre class="has"><code># design &#61; as.data.frame(sample_data(ps))<br />#合并otu表格和tax表格#---------<br />otu_tax &#61; merge(otu_table,tax_table,by &#61; &#34;row.names&#34;,all &#61; F)<br /># head(otu_tax)<br /><br />  #--------添加门类标记&#xff0c;未注释的结果去掉#----<br />  otu_tax$Kingdom &#61; paste(&#34;k&#34;,otu_tax$Kingdom,sep &#61; &#34;__&#34;)<br />  otu_tax$Kingdom[otu_tax$Kingdom &#61;&#61; &#34;k__NA&#34;] &#61;&#34;&#34;<br /><br />  otu_tax$Phylum &#61; paste(&#34;|p&#34;,otu_tax$Phylum,sep &#61; &#34;__&#34;)<br />  otu_tax$Phylum[otu_tax$Phylum &#61;&#61; &#34;|p__NA&#34;] &#61; &#34;&#34;<br /><br />  otu_tax$Class &#61; paste(&#34;|c&#34;,otu_tax$Class,sep &#61; &#34;__&#34;)<br />  otu_tax$Class[otu_tax$Class &#61;&#61; &#34;|c__NA&#34;] &#61; &#34;&#34;<br /><br />  otu_tax$Order &#61; paste(&#34;|o&#34;,otu_tax$Order,sep &#61; &#34;__&#34;)<br />  otu_tax$Order[otu_tax$Order &#61;&#61; &#34;|o__NA&#34;] &#61; &#34;&#34;<br /><br />  otu_tax$Family &#61; paste(&#34;|f&#34;,otu_tax$Family,sep &#61; &#34;__&#34;)<br />  otu_tax$Family [otu_tax$Family &#61;&#61; &#34;|f__NA&#34;] &#61; &#34;&#34;<br /><br />  otu_tax$Genus &#61; paste(&#34;|g&#34;,otu_tax$Genus,sep &#61; &#34;__&#34;)<br />  otu_tax$Genus[ otu_tax$Genus &#61;&#61; &#34;|g__NA&#34;] &#61; &#34;&#34;<br /><br />  otu_tax$Species &#61; paste(&#34;|s&#34;,otu_tax$Species,sep &#61; &#34;__&#34;)<br />  otu_tax$Species[otu_tax$Species &#61;&#61; &#34;|s__NA&#34;] &#61; &#34;&#34;<br /><br />  otu_tax$Row.names &#61; paste(&#34;|t&#34;,otu_tax$Row.names,sep &#61; &#34;__&#34;)<br />  #合并得到结合全部门类的OTU名称#----<br />  OTU_name &#61; paste(otu_tax$Kingdom,otu_tax$Phylum,otu_tax$Class,otu_tax$Order,otu_tax$Family,<br />                   otu_tax$Genus,sep &#61; &#34;&#34;)</code></pre>
  <h3>再整理</h3>
  <p>再有将每一级的名称都加上前面级别的全部注释名称&#xff0c;形成结构。这一步&#xff0c;我们去除了一些特殊的符号。主要是怕影响美观</p>
  <pre class="has"><code>  #----不同等级注释修改#----<br />  otu_tax$Phylum&#61;paste(otu_tax$Kingdom,otu_tax$Phylum,sep &#61; &#34;&#34;)<br />  otu_tax$Class&#61;paste(otu_tax$Phylum,otu_tax$Class,sep &#61; &#34;&#34;)<br />  otu_tax$Order&#61;paste(otu_tax$Class,otu_tax$Order,sep &#61; &#34;&#34;)<br />  otu_tax$Family&#61;paste(otu_tax$Order,otu_tax$Family,sep &#61; &#34;&#34;)<br />  otu_tax$Genus&#61;paste(otu_tax$Family,otu_tax$Genus,sep &#61; &#34;&#34;)<br />  otu_tax$Species&#61;paste(otu_tax$Genus,otu_tax$Species,sep &#61; &#34;&#34;)<br /><br /><br />  #替换两个括号等特殊符号#--------<br />  library(&#34;tidyverse&#34;)<br />  OTU_name &#61; str_replace(OTU_name,&#34;[(]&#34;,&#34;&#34;)<br />  OTU_name &#61; str_replace(OTU_name,&#34;[)]&#34;,&#34;&#34;)<br />  # as.character(OTU_name )<br />  # OTU_name &#61; gsub(&#34;(&#34;,&#34;&#34;,OTU_name[311])<br />  # row.names(otu_tax) &#61; OTU_name<br />  # #将otu表格和tax文件分离#-----<br />  # otu_table &#61; otu_tax[2:(ncol(otu_table)&#43;1)]<br />  # tax_table &#61; otu_tax[(ncol(otu_table)&#43;2):(ncol(otu_table)&#43;2&#43;6)]<br />  #</code></pre>
  <h3>再整理</h3>
  <p>最后按照不同级别数据合并otu表格&#xff0c;然后将注释文件和otu文件合并。</p>
  <pre class="has"><code><br />  #-------将不同分类登记的也添加上去<br />  HA &#61; otu_table<br />  # 按Kingdom合并<br />  grp   merge&#61;cbind(HA, grp)<br />  HA_Kingdom &#61; merge %&gt;% group_by(Kingdom) %&gt;% summarise_all(sum)<br />  colnames(HA_Kingdom)[1]&#61;&#34;Class&#34;<br /><br />  # 按Phylum合并<br />  grp   merge&#61;cbind(HA, grp)<br />  HA_Phylum &#61; merge %&gt;% group_by(Phylum) %&gt;% summarise_all(sum)<br />  colnames(HA_Phylum)[1]&#61;&#34;Class&#34;<br /><br />  # 按Class合并<br />  grp   merge&#61;cbind(HA, grp)<br />  HA_Class &#61; merge %&gt;% group_by(Class) %&gt;% summarise_all(sum)<br />  colnames(HA_Class)[1]&#61;&#34;Class&#34;<br /><br />  # 按Order合并<br />  grp   merge&#61;cbind(HA, grp)<br />  HA_Order &#61; merge %&gt;% group_by(Order) %&gt;% summarise_all(sum)<br />  colnames(HA_Order)[1]&#61;&#34;Class&#34;<br /><br />  # 按Family合并<br />  grp   merge&#61;cbind(HA, grp)<br />  HA_Family &#61; merge %&gt;% group_by(Family) %&gt;% summarise_all(sum)<br />  colnames(HA_Family)[1]&#61;&#34;Class&#34;<br /><br />  # 按Genus合并<br />  grp   merge&#61;cbind(HA, grp)<br />  HA_Genus &#61; merge %&gt;% group_by(Genus) %&gt;% summarise_all(sum)<br />  colnames(HA_Genus)[1]&#61;&#34;Class&#34;<br />  # colnames(otu_tax)<br />  # # 按Species合并<br />  # grp   # merge&#61;cbind(HA, grp)<br />  # HA_Species &#61; merge %&gt;% group_by(Species) %&gt;% summarise_all(sum)<br />  # colnames(HA_Species)[1]&#61;&#34;Class&#34;<br />  ## OTU水平<br />  # merge&#61;cbind(HA, OTU_name)<br />  # head(otu_table)<br />  # HA_OTU &#61; otu_table<br /><br />  # HA_OTU &#61; data.frame(Class &#61; row.names(otu_table),otu_table)<br />  # colnames(HA_OTU )<br />  # head(HA_OTU)<br />  # 合并6个分类级<br />  all &#61; rbind(HA_Kingdom, HA_Phylum, HA_Class, HA_Order, HA_Family, HA_Genus)<br />  dim(all)<br /><br /><br /># head(OTU_name)</code></pre>
  <h3>因为我们去除了未注释出来的&#xff0c;所以会有重复</h3>
  <p>这里只需要简单去除就可以了。我们还构建了phyloseq对象&#xff0c;也就是说基于这个群落我们做其他分析也是是十分方便。</p>
  <pre class="has"><code># 去除重复<br />all &#61; distinct(all, Class, .keep_all &#61; TRUE)<br />dim(all)<br /><br />#-------------lefse分析构建<br />all1 &#61; all<br />row.names(all1) &#61; all1$Class<br />all1$Class &#61; NULL<br />all1 &#61; as.matrix(all1)<br /># head(all1)<br /><br />#-构建phylose对象<br />ps_G_graphlan &#61; phyloseq(otu_table(as.matrix(all1),taxa_are_rows &#61; TRUE),<br />                         sample_data(ps))<br />ps_G_graphlan<br /><br />#----提取OTU表格<br />otu_table &#61; as.data.frame((vegan_otu(ps_G_graphlan)))<br />otu_table[otu_table&#61;&#61;0] <br /># row.names(otu_table)<br /><br /># head(design)<br /><br />design &#61; as.data.frame(sample_data(ps_G_graphlan))</code></pre>
  <h3>如果我们要使用功能python那个脚本运行&#xff0c;保存就可以使用以下代码</h3>
  <p>这里我就不做运行了&#xff0c;之前的帖子相信大家都看过了&#xff0c;我是做了对比的&#xff0c;发现算法都是一致。</p>
  <pre class="has"><code># filename &#61; paste(lefsepath,&#34;/LEFSE_to_run_G_level.txt&#34;,sep &#61; &#34;&#34;)<br /># write.table(otu_table, filename,append &#61; F, quote &#61; F,col.names&#61; F,sep &#61; &#34;\t&#34;)<br />#文件预处理<br /># format_input.py LEFSE_to_run_G_level.txt pri_lefse.in -c 1 -u 2 -o 1000000<br /># ~/src/nsegata-lefse/run_lefse.py pri_lefse.in pri_lefse_2.res  -l 4<br /># plot_res.py pri_lefse_2.res lefse_barplot.pdf --format pdf<br /># plot_cladogram.py pri_lefse_2.res lefse_tree.pdf --format pdf<br /><br /># # 注意这里 –c用来指定分组信息-u 1指定样品信息<br /># #文件分析,这里-l设置LDA阈值&#xff0c;默认为2&#xff0c;我们使用4 会更加严格<br /># ~/src/nsegata-lefse/run_lefse.py pri_lefse.in pri_lefse_2.res  -l 4<br /># #柱状图绘制<br /># plot_res.py pri_lefse_2.res lefse_barplot.pdf --format pdf<br /># #树状图绘制<br /># plot_cladogram.py pri_lefse_2.res lefse_tree.pdf --format pdf<br /># #做每个差异的柱状图<br /># mkdir biomarkers_raw_images<br />## plot_features.py pri_lefse.in pri_lefse_2.res biomarkers_raw_images/</code></pre>
  <h3>R语言lefse分析</h3>
  <p>这部分如果大家不懂得原理建议查看之前的推送&#xff0c;基于R语言实现Lefse分析。</p>
  <pre class="has"><code>#-------------R语言进行lefse分析<br /># ps_sub ps_sub &#61; ps_G_graphlan<br /><br />otu_table &#61; as.data.frame((vegan_otu(ps_sub)))<br />otu_table[otu_table&#61;&#61;0] <br /># head(otu_table)<br /><br />map &#61; as.data.frame(sample_data(ps_sub))<br />otu &#61; (otu_table)<br />claslbl&#61; map$Group<br />set.seed(56290);<br />#KW rank sum test<br />dat3t &#61; otu_table<br /># head(otu)<br /># dat3t &#61; as.matrix(dat3t)<br /># str(dat3t)<br />rawpvalues #--得到计算后得到的p值<br />ord.inx rawpvalues clapvalues # p.adjust<br />dat3t dim(dat3t)<br /><br />wil_datadf # head(wil_datadf)<br />#if no subclass within classes then no wilcoxon rank sum test<br />#Linear Discriminant analysis (LDA)<br />library( MASS)<br />ldares # ldares<br />ldamean # ldamean<br />class_no &lt;ldamean$max ldamean$min <br />#---计算LDA<br />ldamean$LDAscore <br /># head(ldamean)<br /><br />a &#61; rep(&#34;A&#34;,length(ldamean$max))<br />i &#61; 1<br />for (i in 1:length(ldamean$max)) {<!-- --><br />  name &#61;colnames(ldamean[,1:class_no])<br />  a[i] &#61; name[ldamean[,1:class_no][i,] %in% ldamean$max[i]]<br />}<br />ldamean$class &#61; a<br /># head(ldamean)<br /><br />ldamean$Pvalues ldamean$FDR resTable # head(ldamean)<br /># it seems lda add &#96; around names containing dash &#34;-&#34;, need to strip this off<br />rawNms rownames(resTable) # head(resTable)<br /># if(pvalOpt &#61;&#61; &#34;raw&#34;){<!-- --><br />#   de.Num &#61;lda.lvl)<br /># }else{<!-- --><br />p.lvl &#61;0.05<br />lda.lvl &#61; 2<br />de.Num &#61;lda.lvl)<br /># }<br /><br />if(de.Num &#61;&#61; 0){<!-- --><br />  current.msg &lt;}else{<!-- --><br />  current.msg &lt;}<br />current.msg<br /># sort by p value<br />ord.inx resTable #p-values column to appear first; then FDR and then others<br />resTable resTable <br />write.csv(resTable,&#34;cs.csv&#34;)</code></pre>
  <h3>R语言绘制物种分类树</h3>
  <p>这部分我首先进行了颜色的映射数据整理&#xff0c;然后使用microbiomeviz做了圈图的输出。</p>
  <pre class="has"><code>taxtree &#61; resTable[clapvalues&lt;&#61;p.lvl &amp; ldamean$LDAscore&gt;&#61;lda.lvl,]<br /><br />delimiter &#61; &#34;\\|&#34;<br />tax_split row.names(taxtree)<br /># head(taxtree)<br /><br />#-提取所需要的颜色<br />colour &#61; c(&#39;darkgreen&#39;,&#39;red&#39;,&#34;blue&#34;)<br /><br />selececol &#61; colour[1:length(levels(as.factor(taxtree$class)))]<br /><br />names(selececol) &#61; levels(as.factor(taxtree$class))<br />A &#61; rep(&#34;a&#34;,length(row.names(taxtree)))<br /><br />i &#61; 1<br />for (i in 1:length(row.names(taxtree))) {<!-- --><br />  A[i] &#61; selececol [taxtree$class[i]]<br />}<br /><br /># A<br /><br />lefse_lists &#61; data.frame(node&#61;row.names(taxtree),<br />                         color&#61;A,<br />                         stringsAsFactors &#61; FALSE<br />)<br /><br /># str(all)<br /><br />## 计算均值用于呈现结点大小<br />dat <br /># head(dat)<br /># dim(dat)<br /># write.csv(dat,&#34;./tree_tax.csv&#34;)<br /># dat$V2 &#61; NULL<br /># 用物种和丰度生成树骨架<br />tr <br /># tr<br /><br /># 构造树#------<br /><br />p &#61;tree.backbone(tr, size&#61;1,layout&#61; &#39;circular&#39;)<br />p<br /># ?tree.backbone</code></pre>
  <p><img src="https://img-blog.csdnimg.cn/img_convert/9f13345a45487b33185ae4be139bd794.png" alt="9f13345a45487b33185ae4be139bd794.png" /></p>
  <pre class="has"><code># 注释树<br />p p<br /><br />ggsave(&#34;./cs.pdf&#34;,p,width &#61; 10,height &#61; 10)</code></pre>
  <p><img src="https://img-blog.csdnimg.cn/img_convert/dfcf30bdae6d834b14b67bf601ff62a2.png" alt="dfcf30bdae6d834b14b67bf601ff62a2.png" /></p>
  <h3>欢迎加入微生信生物</h3>
  <p><img src="https://img-blog.csdnimg.cn/img_convert/2f9b8076dbe974ee7f3d94b8ca642394.png" alt="2f9b8076dbe974ee7f3d94b8ca642394.png" /></p>
  <h3>快来微生信生物</h3>
  <p>微生信生物</p>
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简介
安装

报错：
Collecting package metadata (current<em>_r</em>epodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current<em>_r</em>epodata.json, will retry with next re</div>
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				<div class="desc ellipsis-online ellipsis-online-1">全局变量是编程<em>语言</em>中常见的一种变量，通过全局定义，可以是某对象函数创建，也可以是本程序任何位置创建，能够被本程序中的所有对象或函数进行引用，全局变量的定义有利于程序的变量共享，简化了添加和修改的程序。

和C<em>语言</em>一样，Python也具有全局变量，其定义全局变量的用法有两种：

1. 声明法

该方法是直接在当前模块中进行全局变量定义声明，<em>使用</em>global的声明方式，再进行引用！


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便捷的方法时：

打开我们的cmd命令

输入Python

输入 import sys

输入 print(sys.path)

列表中的第五个将是你的安装路径


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为啥写这个

平日经常会<em>分析</em>shotgun宏基因组的<em>数据</em>，我们的pipeline<em>使用</em>MetaPhlAn，Kraken等profiler。这种<em>数据</em>经常会产生一个表格，如下

download.file(&quot;https://bitbucket.org/biobakery/bio...</div>
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